Instead of training on a large collection of images, we train a single diffusion model on many large random crops from the input image (typically, about 95% the size of the original image, fig:imageddpmoverview). We find that training on the original resolution of the image is sufficient for generating diverse image samples, even without the use of multi-scale pyramid (unlike most previous single image/video generative methods [1]}, [2]}, [3]}, [4]}, [5]}, [6]}, [7]}). By training on large crops our generated outputs retain the global structure of the input image.
